The Best nilearn Alternatives in 2026

A Python package to assess and improve fairness of machine learning models.

Python package which helps to debug machine learning classifiers and explain their predictions.

An open source Python library for drift detection in machine learning systems.

The LightGBM framework uses gradient boosting for ranking, classification, and other machine learning tasks.

DVCLive logs and tracks machine learning metrics, parameters, and models with Git or DVC.

This Swift library builds machine learning predictions using linear regression.

Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.

modAL provides a modular active learning framework for Python built on top of scikit-learn.

neuropredict evaluates predictive performance using neuroimaging features.

The lightly Python library supports self supervised learning on images.

Pyro brings probabilistic programming capabilities to Python projects using PyTorch.

Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

The Featuretools Python library supports automated feature engineering and is published as open source.

pytorch tabular provides a unified framework for deep learning models on tabular data.

REP provides an IPython-based machine learning environment for consistent, reproducible data-driven research.

Giskard provides open source evaluation and testing tools for LLM agents.

Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

NALP covers Natural Adversarial Language Processing through a dedicated library.

AutoViz automatically creates visualizations for datasets of different sizes from a single line of code.

tsfresh automatically extracts relevant features from time series data.

Snorkel helps generate training data programmatically through weak supervision.

PySS3 supports interpretable text classification with SS3 and visualization tools for explainable AI.

A tool to help you configure, organize, log and reproduce experiments.

AIF360 provides fairness metrics and explanations for datasets and models, along with algorithms to mitigate bias.